Senior ML Engineer

What you'll do

• Build and maintain low-latency, real-time inference services to process heavy security data streams.
• Deploy sophisticated models, including graph analytics and supervised learning, into production environments for threat detection.
• Implement MLOps methodologies including automated CI/CD pipelines, testing protocols, and model performance monitoring.
• Develop internal tooling, feature stores, and libraries to enable the rapid scaling of machine learning services.
• Design and implement features for real-time model scoring using low-latency retrieval techniques.
• Build production systems with an "attacker's mindset" to defend against model evasion and ensure high availability.
• Architect data-driven strategies for threat detection capabilities within the security engineering organization.

What we're looking for

• Experience with streaming services and distributed processing frameworks including Apache Kafka, Flink, Ray, and Spark/Pyspark.
• Proven success implementing MLOps methodologies for high-volume data, including CI/CD, automated deployment, and real-time monitoring.
• Proficiency in containerization using Docker and workflow orchestration via Kubernetes and Apache Airflow.
• Mastery of Python programming with a focus on scalable code design and API development for real-time inference.
• 3 to 5 years of experience in machine learning engineering or data science.
• At least 2 years of experience deploying anomaly detection and clustering systems in production cybersecurity environments.
• Experience implementing feature stores and low-latency feature retrieval techniques for real-time model scoring.
• Ability to lead complex engineering projects, structure data-driven solutions, and work with minimal supervision.